Customer Segmentation using Machine Learning

Abstract

In the competitive landscape of financial services, understanding customer behavior is paramount for effective marketing strategies and personalized services. This study explores customer segmentation techniques applied to credit card holders utilizing a rich dataset comprising demographic, transactional, and behavioral information. Through advanced data analytics, including clustering algorithms and machine learning models, we unveil distinct customer segments based on spending habits, payment behavior, credit utilization, and other relevant features. Our findings reveal several distinct segments within the credit card holder population, each exhibiting unique characteristics and preferences. By delineating these segments, financial institutions can tailor their marketing campaigns, product offerings, and customer service initiatives to better meet the diverse needs of their clientele. Moreover, the segmentation analysis provides insights into risk assessment, fraud detection, and customer retention strategies, thus enhancing overall business performance and customer satisfaction.